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Data Citation: Giving Credit Where Credit is Due

Summary: Citation views: small set of frequent queries to cite arbitrary structured-query results. Three approaches and policies—joint, alternate, aggregated—for citation views are evaluated; experiments show policy choice greatly affects time and size, yielding actionable guidelines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5618
Venue
SIGMOD
Year
2018
Pagerank
5.093636e-05
Overall Rank
11,928 | 18.17%
DOI
10.1145/3183713.3196910

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{wu_sigmod18,
        title = {{Data Citation: Giving Credit Where Credit is Due}},
        author = {Wu, Yinjun and Alawini, Abdussalam and Davidson, Susan B. and Silvello, Gianmaria},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3196910},
        url = {https://dl.acm.org/doi/10.1145/3183713.3196910},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
10,077 ProvCite: Provenance-based Data Citation 2019 VLDB 5.1603976e-05
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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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